Abstract:
In the clustering analysis of intuitionistic fuzzy sets, the traditional clustering algorithm is sensitive to outliers and has high complexity, so it is not suitable for clustering of large-scale intuitionistic fuzzy data. To solve the above problems, an intuitionistic fuzzy clustering algorithm (WIFDPL) based on density peak and weighted Canberra distance is proposed, which can improve the detection accuracy of intuitionistic fuzzy data and reduce the complexity of the algorithm. Since the existing intuitionistic fuzzy distance operator does not satisfy the definition of distance measure, a new intuitionistic fuzzy Canberra distance operator is proposed, which can reduce the deviation degree of data and reduce the sensitivity to outliers. Due to the high complexity of condensed hierarchical clustering algorithm, density peak clustering algorithm is used to cluster intuitionistic fuzzy sets, which greatly improves the running efficiency of the algorithm. Experimental results show that the clustering accuracy is improved by using the improved intuitionistic fuzzy Canberra distance, and the new algorithm is more suitable for clustering large-scale intuitionistic fuzzy sets with lower complexity.
